Abstract
Conventional web-based systems for knowledge sharing cannot help users determine the reliability of an unknown knowledge source on the web. This paper introduces an approach for using the concepts of trust and recommendation from social networks in web-based knowledge sharing systems. A simulation study of three algorithms for calculating trust inferred from recommendations on a social network is presented. The results show that our proposed algorithms can calculate inferred trust values for over 99% of the entities in the network with a high degree of accuracy. Finally, a prototype MAS system that uses trust and recommendation in knowledge sharing is described.
Steven Kraines is the corresponding author. This work is supported by the Japan Science and Technology Agency through the Shippai Chishiki Project, the National Natural Science Foundation of China (70431001) and the Liaoning Province Science and Technology Agency (20061063).
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Guo, W., Kraines, S. (2007). Inferring Trust from Recommendations in Web-Based Knowledge Sharing Systems. In: Wegrzyn-Wolska, K.M., Szczepaniak, P.S. (eds) Advances in Intelligent Web Mastering. Advances in Soft Computing, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72575-6_24
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DOI: https://doi.org/10.1007/978-3-540-72575-6_24
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